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This book explains why applications running on cloud might not deliver the same service reliability, availability, latency and overall quality to.
Table of contents

Service consumers consider the following characteristics to determine quality: Consistency and dependability of the service; the ability of the service to perform its required functions under stated conditions for a specified period Assurance: The level of safety and confidence felt when using the service; security, privacy and risk play into assurance Tangibles: The physical aspects of the service, including user interface device, manuals, hardware, etc.

Making the consumer top priority by understanding consumer needs, offering convenient hours, being available for support when needed, etc. Prompt operation, service and support. Choose a service you deliver that has been brought to your attention for performance concerns. Ask the service owner the following five questions that take into account the RATER aspects of service quality. Ask for justifications for each answer. Implicit within these five questions are the assumptions that you have the means to relate customer perception and expectation with the service, you have a system or tool in place to measure customer expectations for the service, and you have some form of continuous service quality improvement process in place.

The five questions in order of importance are:. For more information about how our training solutions can improve productivity, enhance efficiency and sharpen your competitive edge, visit www. Create an Account Forgot Your Password? What You Need to Know: What You Need to Do: The five questions in order of importance are: Do you have an agreed plan of action to meet customer expectations for the five RATER service quality aspects?

Ask to see the written plan as well as the documented service levels required for each aspect. Similarly, [ 7 , 8 ] focused on security for quality assessment. Furthermore, when performing quality assessment, the quality model has to reflect dramatically changed service conditions and user requirements. However, [ 11 , 12 ] are not considered to newly generate SLA that will have different quality metrics and weight than the previous one. Also, [ 9 , 10 , 13 ] just suggested quality metrics for SaaS cloud computing without including the method for quality assessment.

In Section 3 , we introduce brief and clear definitions about mobile device, mobile cloud computing, and mobile cloud service as follows. Mobile device is defined as devices that have mobility and portability and can use internet generally. The mobile devices have limited hardware conditions.

Mobile cloud computing means the overall technology to provide services from cloud to mobile device. This configuration is responsible for infrastructure. Although the mobile device has fewer resources itself, service customer can use additional functions in cloud server.

Improve Cloud Computing Service Quality with 5 Simple Questions

Thus, mobile device must work given operations simply with its own resources. The following theorems are definitions of mobile cloud computing. CSCs can use a lot of contents and operation software on their mobile devices using their cloud service via internet. Like this, the mobile cloud service means supporting manner and mode of service through cloud infrastructure. Generally, the mobile cloud service requires assurance in functionality for suitable, interoperable services and devices, accurate service delivery, and secure information system and communication, in efficiency for in-time response and resource provisioning on mobile nodes, in usability for operable environment of mobile services, and in reliability for fault tolerant services and resources.

For that reason, we derive the features of mobile cloud services as in Figure 1 and determine the metrics using the derived features for quality assessment in Section 4. As a result, we determined finally the metrics for our quality model after matching with features of mobile cloud service as in Figure 2 and the following are the description of the finally determined metrics.

Functionality is a set of attributes that bear on the existence of a set of functions and their specified properties. In this paper, functionality denoted by FU that is the metric for the providing degree of functionality meets expressed or suggested needs in a certain condition when the services are provided. In other words, it is a metric about accuracy and suitability to measure whether the mobile cloud services are correctly provided. As the user needs, mobile cloud service has the responsibility of serving accurate outputs and making it easy to complete the function.

Suitability is an attribute that bears on the presence and appropriateness of a set of functions for specified tasks. For calculating the value of this metric, we define a term SU denoting suitability as in the following equation: Accuracy accurateness is an attribute that bears on the provision of right or agreed results or effects. For calculating value of this metric, we define a term AC denoting accuracy as in the following equation: Interoperability is an attribute that bears on its ability to interact with specified systems.

For calculating the value of this metric, we define a term IO denoting interoperability as in the following equation: Security is an attribute that bears on its ability to prevent unauthorized access or alteration, whether accidental or deliberate, to programs or data.

Decision Brief

For calculating the value of this metric, we define a term SEC denoting security as in the following equation when the problem happens: Reliability is a set of attributes that bear on the capability of software to maintain its level of performance under stated conditions for a stated period of time. Reliability denoted by RE is the metric that most mobile cloud services served on the mobile devices and all the user data will be stored in cloud storage through network. Reliability is an important metric for the service quality evaluation as mobile cloud services are depending on network conditions.

Reliability has several submetrics such as maturity, fault tolerance, and recoverability.

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Fault tolerance is the property that enables a system to continue operating property in the event of the failure of or one or more faults within some of its components. In the reliability, we define a term of FT denoting fault tolerance that can be calculated as in the following equation: Usability is a set of attributes that bear on the effort needed for use and on the individual assessment of such use by a stated or implied set of users.

Usability denoted by US is the degree to which a software or service can be used by specified users to achieve quantified objectives with effectiveness, efficiency, and satisfaction in a quantified context of use. For the mobile cloud service, usability is the metric to evaluation of learnability, operability, understandability, and so forth. When consumer uses mobile cloud services, it must be easy to control and access the service and give user satisfaction. As in following equation, how operability can measure many proper functions are provided to user through their mobile cloud service operation and control: Efficiency is a set of attributes that bear on the relationship between the level of performance of the software and the amount of resources used, under stated conditions.

Efficiency is a metric to measure the relative performance for used service amount in regulated condition. And this is the metric to assess time behavior TB and resource utilization RU for mobile cloud services. Time behavior is an attribute that bears on response and processing times and on throughput rates in performance of its function. So, this metric measures a ratio of an execution time for a total invocation time. We define a term TB that denotes time behavior to calculate value of this metric as in the following equation: Resource utilization is an attribute that bears on the amount of resource used and the duration of such use in performing its function.

And this metric measures a ratio of an amount of allocated resources for the predefined resources.

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So, for calculating the value of this metric, we defined a term RU that denotes resource utilization as in the following equation: When the mobile cloud service is provided, clients must satisfy the requirements such as response time and throughput by well utilizing the resources.

Due to poor resource provisioning, the service quality does not need to degrade SLA. For that reason, time behavior and resource utilization are chosen for calculating value of efficiency. Portability is a set of attributes that bear on the ability of software to be transferred from one environment to another.

Portability is the usability of the same software in different environment. The prerequirement for portability is the generalized abstraction between the application logic and system interfaces. When software or service with the same functionality is produced for several platforms, portability is the key issue for development cost reduction. And its metrics are for evaluation of adaptability and installability in the mobile cloud service.

It must be effectively adapted on various service environments and devices and it also should be easy to install and remove. Maintainability is a set of attributes that bear on the effort needed to make specified modifications. Maintainability is also important metric for the mobile cloud services. In this metric, Analyzability, Changeability, Stability, Testability and Maintainability compliance are included for its submetrics.

Service Quality of Cloud-Based Applications

However, the submetrics are difficult to map to mobile cloud service requirements. Thus, for that reason, we exclude portability and maintainability in quality evaluation model, mCSQAM. In this paper, we propose a quality assessment model to validate service quality and recommend the best service by cloud broker environments which is easier way to customer to deliver proper services among many cloud providers.

AHP provides alternatives through reasonable evaluation that provides systematic analysis and stepwise derivation with pairwise comparison about various measures. Utilizing mathematical methodology, quantitative evaluation measures cannot be only considering but also qualitative assessment measures by AHP. Furthermore, it has been widely used for decision-making with various and complex measures due to simple calculation and easy understanding. To make a decision in an organized way to generate priorities, we need to decompose the decision into the following 4 steps: Each element in an upper level is used to compare the elements in the level immediately below with respect to it.

Do this for every element. Then, for each element in the level below add its weight values and obtain its overall or global priority. Continue this process of weighting and adding until final priorities of the alternatives in the bottom mist level are obtained.


By using the assessment result, QB controls quality metrics weight for balanced service quality. Furthermore, the newly generated SLA is also used for the next time quality assessment. We evaluate our model and show the result of quality assessment using generating service scenarios, whose services have different quality related components as shown in Figure 4.

For the case study, we assume that the service quality value of each mobile cloud service as schematically given as shown Figure 4. And we also assume that each service has different weight to assess in detail for the case study. Through the above assumptions, we can find which metric is more effective to 4 different mobile cloud services quality and compare them relatively. Step 1 applying weight to each quality metric. We assigned a weight value to each submetric in Functionality FU as shown in Table 1.

Submetrics of efficiency EF also are assigned as shown in Table 2. Although weight value can vary depending on user requirements, it is difficult to use the weight for general cases. Thus, we assume that the metrics have fixed weight at the design time in this paper. However, in order to compare the result of quality assessment in different cases and apply properly to the real case, we evaluate our model by assigning different weight values with 4 different scenarios.

mCSQAM: Service Quality Assessment Model in Mobile Cloud Services Environment

So now we can have 4 different weighted scenario services as shown in Figure 4. After applying weights, mCSQV mobile cloud service quality value is finally calculated using the product of service quality measure value and weight value as in the following equation: Step 2 calculating the result after applying weight for sub-metrics. Submetrics of FU have weight values as in Table 1. In evaluation, weight values of submetrics are randomly determined as shown Our model can support adaptation of dynamic changes by user or service requirements.

After applying the above settings, we have the quality value as in following equation for 4 different scenario cases. The weight of FT, submetric of RE, is 1 as having only one submetric. After the calculation of this case, each service has a value of 0. If a user or a service considered only reliability, the best service is Service 2. The weight of OP, submetric of US, is 1 as a single submetric. After the calculation of this case, each service has the value as in the following equation for 4 different scenario cases.

And the value 0.

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